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Network simulations of self-sustained activity in networks of adaptive exponential integrate and fire neurons
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Demo files implemented using both NEURON and PyNN.
demo_cx-lts
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Simulations of self-sustained AI states in a small N=500 network of
excitatory and inhibitory neurons, described by Adaptive
Exponential (Brette-Gerstner-Izhikevich) type neurons with
exponential approach to threshold. The connectivity is random and
there is a small proportion (5%) of LTS cells among the excitatory
neurons. This simulation reproduces Fig. 7 of the paper below.
demo_cx_up-down
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Simulations of Up-Down states in a two-layer cortical network, with
one N=2000 network and a smaller N=500 network. Both networks have
excitatory and inhibitory neurons described by Adaptative
Exponential (Brette-Gerstner-Izhikevich) type neurons with
exponential approach to threshold. The connectivity is random
within each network as well as between them. In the N=500 network,
there is a small proportion (5%) of LTS cells among the excitatory
neurons. This simulation reproduces Fig. 13 of the paper below.
See details in the following article:
Destexhe, A. Self-sustained asynchronous irregular states and
Up/Down states in thalamic, cortical and thalamocortical networks
of nonlinear integrate-and-fire neurons. Journal of Computational
Neuroscience 27: 493-506, 2009.
arXiv preprint: http://arxiv.org/abs/0809.0654
Original NEURON implementation by Alain Destexhe
destexhe@unic.cnrs-gif.fr
http://cns.iaf.cnrs-gif.fr
Converted to PyNN by Andrew Davison
davison@unic.cnrs-gif.fr
and Lyle Muller
lyle.e.muller@gmail.com
Usage (NEURON version):
nrnivmodl
nrngui <file.oc>
Usage (Python version):
python <file.py> <simulator>
where <file.py> is one of the demo files, and <simulator>
is one of neuron, nest, pcsim, brian, facets_hardware2, etc...